

import sys
from collections import defaultdict



class 						_A     :





  '''simple docstring'''



  def __init__(  self    ):

      '''simple docstring'''
      snake_case							:		str        =		[]





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      return self.node_position[vertex]





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		List[Any]        =		pos





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      if start > size // 2 - 1:
          return
      else:
          if 2 * start + 2 >= size:
              snake_case							:		Any        =		2 * start + 1
          else:
              if heap[2 * start + 1] < heap[2 * start + 2]:
                  snake_case							:		Any        =		2 * start + 1
              else:
                  snake_case							:		Any        =		2 * start + 2
          if heap[smallest_child] < heap[start]:
              snake_case      ,		snake_case							:		Union[str, Any]        =		heap[smallest_child], positions[smallest_child]
              snake_case      ,		snake_case							:		List[Any]        =		(
                  heap[start],
                  positions[start],
              )
              snake_case      ,		snake_case							:		List[Any]        =		temp, tempa

              snake_case							:		Dict        =		self.get_position(positions[smallest_child]    )
              self.set_position(
                  positions[smallest_child]   ,self.get_position(positions[start]    )    )
              self.set_position(positions[start]   ,SCREAMING_SNAKE_CASE_    )

              self.top_to_bottom(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		List[Any]        =		position[index]

      while index != 0:
          snake_case							:		int        =		int((index - 2) / 2    ) if index % 2 == 0 else int((index - 1) / 2    )

          if val < heap[parent]:
              snake_case							:		Optional[Any]        =		heap[parent]
              snake_case							:		str        =		position[parent]
              self.set_position(position[parent]   ,SCREAMING_SNAKE_CASE_    )
          else:
              snake_case							:		Optional[Any]        =		val
              snake_case							:		Tuple        =		temp
              self.set_position(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )
              break
          snake_case							:		Optional[Any]        =		parent
      else:
          snake_case							:		Tuple        =		val
          snake_case							:		Union[str, Any]        =		temp
          self.set_position(SCREAMING_SNAKE_CASE_   ,0    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		Any        =		len(SCREAMING_SNAKE_CASE_    ) // 2 - 1
      for i in range(SCREAMING_SNAKE_CASE_   ,-1   ,-1    ):
          self.top_to_bottom(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,len(SCREAMING_SNAKE_CASE_    )   ,SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		Optional[Any]        =		positions[0]
      snake_case							:		int        =		sys.maxsize
      self.top_to_bottom(SCREAMING_SNAKE_CASE_   ,0   ,len(SCREAMING_SNAKE_CASE_    )   ,SCREAMING_SNAKE_CASE_    )
      return temp







def       lowercase					(	__A :					str		)				->			Optional[Any]:





    '''simple docstring'''




    snake_case							:		Union[str, Any]        =		Heap()

    snake_case							:		int        =		[0] * len(a_		)
    snake_case							:		Any        =		[-1] * len(a_		)  # Neighboring Tree Vertex of selected vertex
    # Minimum Distance of explored vertex with neighboring vertex of partial tree
    # formed in graph
    snake_case							:		Union[str, Any]        =		[]  # Heap of Distance of vertices from their neighboring vertex
    snake_case							:		List[str]        =		[]

    for vertex in range(len(a_		)		):
        distance_tv.append(sys.maxsize		)
        positions.append(a_		)
        heap.node_position.append(a_		)

    snake_case							:		Tuple        =		[]
    snake_case							:		Optional[Any]        =		1
    snake_case							:		Any        =		sys.maxsize
    for neighbor, distance in adjacency_list[0]:
        snake_case							:		Tuple        =		0
        snake_case							:		Optional[Any]        =		distance
    heap.heapify(a_ ,    a_		)

    for _ in range(1 ,    len(a_		)		):
        snake_case							:		Any        =		heap.delete_minimum(a_ ,    a_		)
        if visited[vertex] == 0:
            tree_edges.append((nbr_tv[vertex], vertex)		)
            snake_case							:		Tuple        =		1
            for neighbor, distance in adjacency_list[vertex]:
                if (
                    visited[neighbor] == 0
                    and distance < distance_tv[heap.get_position(a_		)]
                ):
                    snake_case							:		Any        =		distance
                    heap.bottom_to_top(
                        a_ ,    heap.get_position(a_		) ,    a_ ,    a_		)
                    snake_case							:		Any        =		vertex
    return tree_edges


if __name__ == "__main__":  # pragma: no cover
     # < --------- Prims Algorithm --------- >
     __lowercase     : Any       =						int(input('''Enter number of edges: ''').strip())
     __lowercase     : Dict       =						defaultdict(list)
     for _ in range(edges_number):
          __lowercase     : Optional[int]       =						[int(x) for x in input().strip().split()]
          adjacency_list[edge[0]].append([edge[1], edge[2]])
          adjacency_list[edge[1]].append([edge[0], edge[2]])
     print(prisms_algorithm(adjacency_list))









'''simple docstring'''


# Usage:
# ./gen-card-facebook-wmt19.py

import os
from pathlib import Path


def     SCREAMING_SNAKE_CASE							(   a_						:				str					,       a_						:				Union[str, Any]					,       a_						:				Dict      ):
  __a								=       {
      'en': 'Machine learning is great, isn\'t it?',
      'ru': 'Машинное обучение - это здорово, не так ли?',
      'de': 'Maschinelles Lernen ist großartig, oder?',
  }

  # BLUE scores as follows:
  # "pair": [fairseq, transformers]
  __a								=       {
      'ru-en': ['[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)', '39.20'],
      'en-ru': ['[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)', '33.47'],
      'en-de': ['[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)', '42.83'],
      'de-en': ['[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)', '41.35'],
  }
  __a								=       f"{src_lang}-{tgt_lang}"

  __a								=       f"\n---\nlanguage: \n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- facebook\nlicense: apache-2.0\ndatasets:\n- wmt19\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.\n\nFor more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).\n\nThe abbreviation FSMT stands for FairSeqMachineTranslation\n\nAll four models are available:\n\n* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)\n* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)\n* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)\n* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = \"facebook/wmt19-{src_lang}-{tgt_lang}\"\ntokenizer = FSMTTokenizer.from_pretrained(mname)\nmodel = FSMTForConditionalGeneration.from_pretrained(mname)\n\ninput = \"{texts[src_lang]}\"\ninput_ids = tokenizer.encode(input, return_tensors=\"pt\")\noutputs = model.generate(input_ids)\ndecoded = tokenizer.decode(outputs[0], skip_special_tokens=True)\nprint(decoded) # {texts[tgt_lang]}\n\n```\n\n#### Limitations and bias\n\n- The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)\n\n## Training data\n\nPretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).\n\n## Eval results\n\npair   | fairseq | transformers\n-------|---------|----------\n{pair}  | {scores[pair][0]} | {scores[pair][1]}\n\nThe score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support:\n- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).\n- re-ranking\n\nThe score was calculated using this code:\n\n```bash\ngit clone https://github.com/huggingface/transformers\ncd transformers\nexport PAIR={pair}\nexport DATA_DIR=data/$PAIR\nexport SAVE_DIR=data/$PAIR\nexport BS=8\nexport NUM_BEAMS=15\nmkdir -p $DATA_DIR\nsacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target\necho $PAIR\nPYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS\n```\nnote: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.\n\n## Data Sources\n\n- [training, etc.](http://www.statmt.org/wmt19/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)\n\n\n### BibTeX entry and citation info\n\n```bibtex\n@inproceedings{{...,\n  year={{2020}},\n  title={{Facebook FAIR's WMT19 News Translation Task Submission}},\n  author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},\n  booktitle={{Proc. of WMT}},\n}}\n```\n\n\n## TODO\n\n- port model ensemble (fairseq uses 4 model checkpoints)\n\n"
  os.makedirs(a_					,       exist_ok=a_      )
  __a								=       os.path.join(a_					,       'README.md'      )
  print(f"Generating {path}"      )
  with open(a_					,       'w'					,       encoding='utf-8'      ) as f:
    f.write(a_      )


# make sure we are under the root of the project
UpperCAmelCase_					=   Path(__file__).resolve().parent.parent.parent
UpperCAmelCase_					=   repo_dir / "model_cards"

for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
  UpperCAmelCase_		,	UpperCAmelCase_		,	UpperCAmelCase_					=   model_name.split("-")
  UpperCAmelCase_					=   model_cards_dir / "facebook" / model_name
  write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)



